rescomp

rescomp is an R package that supports the definition, simulation and visualization of ODE models of ecological consumer-resource interactions. In essence, it is a consumer-resource modelling focused interface to the excellent deSolve package.

Installation

You can install rescomp from GitHub with:

# install.packages("devtools")
devtools::install_github("andrewletten/rescomp")

Example

The primary user function in rescomp is spec_rescomp(), which facilitates: i) the definition and parameterisation of a desired consumer-resource model, and ii) the specification of simulation parameters. The default output from spec_rescomp() is a list defining a model for a single type I consumer (linear functional response) and a single continuously supplied resource (e.g., in a chemostat).

pars <- spec_rescomp()
#> Model properties 
#>  * 1 consumer(s) and 1 resource(s)
#>  * Consumers have type 1 functional responses
#>  * Resource supply is continuous (e.g. chemostat)
#>  * Mortality is continuous
#> 
#> Simulation properties 
#>  * Simulation time: 1000 time steps
#>  * Init state: consumer(s) = [10], resource(s) = [1]

plot_funcresp() plots the functional response for easy visualistion prior to running a simulation.

The model is then simulated via sim_rescomp() (effectively a wrapper for deSolve::ode() with convenient defaults).

m1 <- sim_rescomp(pars)

Output dynamics can be visualised with plot_rescomp().

Note, the core rescomp functions are compatible with pipes. For example spec_rescomp() |> sim_rescomp() |> plot_rescomp() will output the plot above.

The main utility of rescomp comes with specifying more elaborate models and simulation dynamics. Features/options include (but are not limited to):

  • Unlimited number of consumers/resources
  • Consumer functional response (type I, II or III)
  • Resource dynamic (chemostat, logistic and/or pulsed)
  • Resource type (substitutable or essential)
  • Continuous or intermittent mortality (e.g. serial transfer)
  • Time dependent growth and consumption parameters
  • Delayed consumer introduction times

See ?spec_rescomp for all argument options.

The following two examples demonstrate how to build and simulate a model for: i) two consumers with type II functional responses on a single logistically growing resources; and ii) two consumers with type III functional responses with pulsed resources and time dependent growth parameters. A wide range of other examples can be found in the package vignettes.

Example 1 

pars <- spec_rescomp(
  spnum = 2, 
  resnum = 1,
  funcresp = "type2",
  mumatrix = matrix(c(0.7,0.05), 
                    nrow = 2, 
                    ncol = 1,
                    byrow = TRUE),
  kmatrix = matrix(c(2, 0.015), 
                   nrow = 2, 
                   ncol = 1, 
                   byrow = TRUE),  
  chemo = FALSE,
  resspeed = 3,
  resconc = 0.2,
  totaltime = 2000
)
#> Model properties 
#>  * 2 consumer(s) and 1 resource(s)
#>  * Consumers have type 2 functional responses
#>  * Resources grow logistically
#>  * Mortality is continuous
#> 
#> Simulation properties 
#>  * Simulation time: 2000 time steps
#>  * Init state: consumer(s) = [10, 10], resource(s) = [0.2]
plot_funcresp(pars, maxx = 0.2)

m2 <- sim_rescomp(pars)
plot_rescomp(m2)

Example 2 

pars <- spec_rescomp(
  spnum = 2, 
  resnum = 2,
  funcresp = "type3",
  timepars = TRUE,
  timeparfreq = 40,
  mumatrix = list(matrix(c(0.4,0.1,
                           0.05, 0.02), 
                    nrow = 2, 
                    ncol = 2,
                    byrow = TRUE),
                  matrix(c(0.2, 0.1,
                           0.5, 0.3), 
                    nrow = 2, 
                    ncol = 2,
                    byrow = TRUE)),
  resspeed = 0,
  rinit = c(1, 1),
  respulse = 1,
  pulsefreq = 40,
  totaltime = 1000
)
#> Model properties 
#>  * 2 consumer(s) and 2 resource(s)
#>  * Consumers have type 3 functional responses
#>  * Resources are substitutable
#>  * Resource supply is pulsed only
#>  * Mortality is continuous
#>  * Time dependent parameters with instantaneous switching every 40 timesteps
#> 
#> Simulation properties 
#>  * Simulation time: 1000 time steps
#>  * Resources pulsing every 40 timesteps
#>  * Init state: consumer(s) = [10, 10], resource(s) = [1, 1]
plot_funcresp(pars, maxx = 1)

m3 <- sim_rescomp(pars)